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[Previously saved workspace restored]

> # Script to run QAP
> library(sna)
Loading required package: statnet.common

Attaching package: ‘statnet.common’

The following objects are masked from ‘package:base’:

    attr, order

Loading required package: network

‘network’ 1.18.1 (2023-01-24), part of the Statnet Project
* ‘news(package="network")’ for changes since last version
* ‘citation("network")’ for citation information
* ‘https://statnet.org’ for help, support, and other information

sna: Tools for Social Network Analysis
Version 2.7-1 created on 2023-01-24.
copyright (c) 2005, Carter T. Butts, University of California-Irvine
 For citation information, type citation("sna").
 Type help(package="sna") to get started.

> library(doParallel)
Loading required package: foreach
Loading required package: iterators
Loading required package: parallel
> library(doRNG)
Loading required package: rngtools
> 
> # Load the followers adjacency matrix
> y <- readRDS("processed_data/followers_adjacencyMatrix.Rds")
> # Load the predicting matrix 
> load("processed_data/QAP_predicting_matrices.RData")
> 
> diag(y) <- NA
> 
> y_v <- c(y)
> 
> 
> Var.names <-c("State_Similarity",
+                "Party_Similarity",
+                "Chamber_Similarity",
+                "Gender_Similarity",
+                "Race_Similarity",
+                "Difference_in_Legislatures_Profeshionalism",
+                "Dem_Sender_Effect",
+                "Rep_Sender_Effect",
+                "House_Sender_Effect",
+                "Female_Sender_Effect",
+                "Profesh_Sender_Effect",
+                "Black_Sender_Effect",
+                "Latino_Sender_Effect",
+                "Asian_Sender_Effect",
+                "Mena_Sender_Effect",
+                "Multi_Sender_Effect",
+                "Native_Sender_Effect",
+                "Democrat_Receiver_Effect",
+                "Republican_Receiver_Effect",
+                "House_Receiver_Effect",
+                "Female_Receiver_Effect",
+                "Profesh_Receiver_Effect",
+                "Black_Receiver_Effect",
+                "Latino_Receiver_Effect",
+                "Asian_Receiver_Effect",
+                "Mena_Receiver_Effect",
+                "Multi_Receiver_Effect",
+                "Native_Receiver_Effect",
+                "Same_Party_Same_State",
+                "Same_Chamber_Same_State",
+                "Same_Gender_Same_State",
+                "Same_Race_Same_State",
+                "Contiguous_States")
> 
> for(i in 1:length(Var.names)){
+   xi <- predicting_matrices[i,,]
+   diag(xi) <- NA
+   print(sum(is.na(xi)))
+   if(i == 1){
+     xdf <- cbind(c(xi))
+   }
+   if(i > 1){
+     xdf <- cbind(xdf,cbind(c(xi)))
+   }
+ }
[1] 4108
[1] 4108
[1] 4108
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[1] 4108
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> 
> send_id <- matrix(1:nrow(y),nrow(y),nrow(y))
> rec_id <- t(send_id)
> diag(send_id) <- NA
> diag(rec_id) <- NA
> 
> xdf <- cbind(xdf,c(send_id),c(rec_id))
> 
> xdf <- data.frame(xdf)
> 
> names(xdf) <- c(Var.names,c("send_id","rec_id"))
> 
> xydf <- data.frame(y_v,xdf)
> 
> xynd <- na.omit(xydf)
> 
> covs <- paste(Var.names,collapse="+")
> 
> names(xynd)[1] <- "yij"
> 
> library(fastglm)
Loading required package: bigmemory
> system.time(est0 <- fastglm(as.matrix(xynd[,Var.names]),xynd$yij,family=binomial(),method=3))
   user  system elapsed 
 26.738   9.955  38.515 
> 
> prtie <- est0$fitted.values
> 
> nnet_sim <- 100
> 
> set.seed(92011)
> 
> sim_els <- list()
> for(i in 1:nnet_sim){
+   utie <- runif(length(prtie))
+   edgesi <- which(prtie > utie)
+   el_i <- xynd[edgesi,c("send_id","rec_id")]
+   sim_els[[i]] <- el_i
+   if(i/10==round(i/10)) print(i)
+ }
[1] 10
[1] 20
[1] 30
[1] 40
[1] 50
[1] 60
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[1] 80
[1] 90
[1] 100
> 
> save(list="sim_els",file="simulated_follower_networks.RData")
> 
> 
> 
> 
> 
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
 98.949  28.481 135.146 
